Why Swinging More Won't Help Chris Davis

Previously, I showed how Chris Davis propensity to strike out decreases his overall offensive production despite a decent walk rate. I made two questionable assumptions in that article. I assumed that players will hit the same proportion of pitches in the strike zone regardless of swing rate. I also assumed that production per pitch put into play would remain constant regardless of swing rate. These assumptions seem to be counterintuitive. Batters that swing less frequently are expected to swing at only the best pitches. It makes sense to take a closer look to see how batters perform.

In order to measure this, I used a pitchf/x dataset with data from 2013, 2014 and 2015 and determined all batters that faced 1,000 pitches or more in a given season. For each batter, I determined their monthly performance in a given season for all months from April to September while discarding players that didn’t play in each month from the dataset. Then, I sorted each player’s monthly performance in a season by swing rate. Finally, I grouped the players by that ranking, so that the month when a player had his lowest swing rate in a season was ranked 1 and the month with their highest swing rate was ranked 6.

For example, look at Adam Jones in 2013. He had his lowest swing rate in April, so that month was ranked “1”. His next lowest swing rate was in July, and therefore that month was ranked “2”. The next lowest was September, so that month was ranked “3” and etc. His monthly performance in a given year is placed in a different category based on how his swing rate in that month compares to his swing rate in other months.

For Chris Davis, I looked solely at 2013 and 2015 to avoid being biased by poor performance in 2014 and ranked his performance by month into two groups based on swing rate with six months in a low swing group and six months in a high swing group. This methodology allows one to compare players to themselves and measure the impact that reducing or increasing swing rate has on the results.

This first chart shows there’s considerable variation in a batters’ swing rate over a given year as measured in units of months. Every batter is in each group once and only once, but Swing% goes from 42.77% in the group where batters swing least frequently to 51.43% in the group where batters swing most frequently. As swing rate decreases, the chance of a called ball increases while the chance of a strike slightly decreases. The ratio of called balls to called strikes remains constant. As swing rate decreases, called ball rate increases and strike rate stays static.

This second chart shows the frequency of where pitchers throw the ball and when batters swing and put the ball into play. As swing rate goes down, batters swing less at pitches in all areas of the strike zone. Batters do put a larger proportion of pitches in the strike zone into play as swing rate decreases but the absolute impact is minimal. Batters put few pitches out of the strike zone into play regardless of swing rate and therefore it’s going to have only a small impact on actual production. Players that swing less will put a higher proportion of pitches in the strike zone into play than pitches outside of the strike zone, but the impact is minimal in absolute terms.

This third chart shows production based on grouping. As swing rate decreases, walk rate increases while strikeout rate remains constant leading to large increases in “wOBA Not In Play”.  This makes sense because the called ball rate is increasing considerably while in play rate is decreasing as swing rate goes down resulting in more walks. Meanwhile, strike/foul rate decreasing suggests there should be fewer strikeouts while in play rate decreasing, suggests there should be more strikeouts. It would seem these two factors cancel each other out.

Also note how the increase in both BB% and “wOBA Not In Play” is reasonably linear and is split reasonably evenly among all categories. This is what one would expect to see if swing rate has an impact on wOBA Not in Play. It should come as no surprise that there’s a moderate correlation of -.5 between Swing% and wOBA Not in Play.

There’s only a small increase in wOBA in play. It’s constant when pitches aren’t thrown in the strike zone and there is only a slight change when pitches are thrown in the strike zone. These differences aren’t particularly linear or split evenly among all categories. It should come as no surprise that even the correlation between Swing% and wOBA In Play for strikes is .03. In all likelihood, the difference in wOBA in play between these groups is probably nothing more than luck. This indicates that production of pitches put into play is largely independent of swing rate.

One might think the two assumptions made above could have huge implications, but it turns out that one has a minimal effect and the other has no effect.

Chris Davis’ stats tell a similar but unique story. He has a well above average called ball/called strike ratio regardless of whether he’s being aggressive or not. But his contact tool is so poor that being aggressive does little to help him put pitches into play. The Hardball Times noted that Chris Davis' "True Contact" rate is one of the worst in the majors (462 out of 498). In fact, his contact tool can be described by this:

Despite a 4% increase in swing rate between his most aggressive months and most selective months, he only had a .27% increase in in play rate. This suggests that he only put 6.8% of these extra pitches into play, which is less than half of his base rate.

This next chart shows that pitchers are only throwing roughly 34-35% of pitches to him in the strike zone compared to a standard of 38-40%. He does do a good job swinging at strikes rather than balls suggesting that he has an above-average eye.

But if pitchers are going to give him relatively few pitches to hit, then he needs to swing at relatively few pitches. Otherwise, his aggression just results in a lot of fouled pitches, swinging strikes and ultimately strike outs. He should probably be swinging closer to 37% of the time instead of 47%. This would likely result in more walks, fewer strikeouts and being thrown a larger proportion of strikes.

This third chart shows his actual results. He swings so frequently and has such a poor contact tool that a reduction in swinging resulted in fewer strikeouts and more walks. While players typically want to put pitches into play, an increase of BB% by 3.25 and a decrease of K% by 2.14 is an extremely favorable trade for every batter that will ever play baseball.

The data indicate that he has better results when putting pitches into play when he swings often. This is counter to the results in the original data set and could be a reason for him to swing frequently. Alternatively, it could be a function of small sample size as he only put roughly 360 pitches into play total in both sets of six months.

Swinging more often isn’t likely to help him improve by much. Suppose we go back into time and make the following change. For each at bat that he doesn’t put the ball into play, we decide that he will swing at every pitch in the strike zone that he didn’t actually swing at in real life. Given his inability to make contact, such a change would only have a limited impact on his overall production. His walk rate would drop from about 10% to 9%, his strikeout rate would drop from roughly 31% to 25% and his wOBA would increase by only 6%. This shows that a higher swing rate, even presuming it would occur in only potentially beneficial situations, would have minimal impact on his production.

The above analysis shows that production per pitch put into play is largely independent of swing rate for the average batter. It is possible that hitters that swing at an extremely high or low percent of pitches do better or worse than the average. The assumption that I made in my article that Adam Jones would have a constant wOBA In Play regardless of swing rate was close enough to accurate.

This analysis also illustrates how poor Chris Davis is at making contact. Such a weakness makes it unlikely that he’ll ever be able to make average levels of contact even if he swings at extremely high levels. His aggressiveness pretty much solely results in more swinging strikes and fouls rather than pitches put into play. Therefore, he should consider swinging less frequently in order to increase his walk rate and likely decrease his strikeout rates. The average player may do better when they put the ball into play rather than when they don’t, but contact simply isn’t in Chris Davis’ toolbox. Players with extreme power but limited plate discipline would seem to be more successful with an extremely selective approach. And, like Mr. Burns, at Camden Depot we like to play the percentages.

Adam Jones Has Better Plate Discipline Than Chris Davis

The Orioles recently re-signed Chris Davis to a seven-year, $161 million contract. As Jon wrote, Davis brings to the immediate a strong bat with a heavy right-handed favored split.  It’s pretty simple, people like Chris Davis because he has elite power and can absolutely crush pitches. Meanwhile, CBS Sports feels that he still strikes out a lot, but made small strides with his plate discipline last season.  The writer argues that Davis’s BB/K ratio of .4 may not be ideal but isn’t terrible either. People generally seem to agree that Chris Davis may strike out a lot but his walk rate does offset it to some extent.

On the other hand, it is pretty well accepted that Adam Jones has bad plate discipline. Bucs Dugout asserts that Adam Jones swings at pretty much everything. Peter Gammons claims that his major problem is that he swings too often in favorable counts. Camden Chat says that it’s indisputable that he strikes out too much and walks too little.

In this article, I intend to show that Chris Davis has significantly worse plate discipline than Adam Jones, despite the fact that Davis has a BB/K of roughly .4 while Jones has a BB/K of roughly .2 over the past three years.  Here, let me explain.

Recently, I wrote a post breaking wOBA into three component parts using data from ESPN Stats and Information. I measured a player’s production when he hits a pitch in the strike zone, when he hits a pitch that isn’t in the strike zone, and when he doesn’t make contact at all. Using this method measures each player’s production in these three areas as shown in the chart below.

Chris Davis averages a wOBA of .187 on balls not put into play compared to Adam Jones who averages a .136 on balls not put into play from 2013-2015. This makes sense as Chris Davis does walk more often than Adam Jones and would seem to support the argument that Davis has better plate discipline. However, such an analysis ignores one crucial thing. It ignores the fact that Adam Jones failed to put the ball into play 24% of the time while Chris Davis failed to do so 44% of the time.

This is relevant due to a point that I made in my previous post. For the average batter, a strikeout is more damaging than a walk is beneficial. Batters only have a .219 wOBA on average when failing to put the ball into play and nearly all do better when they put the ball into play than when they fail to do so. The general rule of thumb is that it takes roughly 11 walks to offset 9 strikeouts because batters average a .369 wOBA when putting the ball into play.

In order to compare Adam Jones’ plate discipline to Chris Davis, it is necessary to take into account the fact that Chris Davis doesn’t put the ball into play as often.  Just comparing .187 to .136 fails to take quantity into account.

The way to take into account both quality and quantity requires some high school algebra. It requires comparing how Adam Jones actually performed to how he’d perform if he put a pitch into play only in 56% of PAs and failed to do so in the other 44% but had the same wOBA when not putting the same ball into play as Chris Davis. In other words, this is equivalent of determining which equation is greater:

.76 * wOBA Jones InPlay + .24 *wOBA Jones NotInPlay  or
.56 * wOBA Jones InPlay  + .44 *wOBA CD NotInPlay.
Which simplifies to:
.2 * wOBA AJ InPlay + .24 wOBA AJ NotInPlay vs .44 * CD wOBA NotInPlay

In other words, it’s necessary to compare how Chris Davis performs during the 44% of times when he fails to put the ball into contact to how Adam Jones performs during the 24% of times he fails to put the ball into contact AND another 20% of how he performs when he does put the ball into contact. This results in the following.

When one takes both quantity and quality into account, it becomes apparent that a player able to hit the ball as successfully as Adam Jones would rather have his walk and strikeout numbers rather than having Chris Davis’s. Even though Chris Davis has a better BB/K and a wOBA Not in Play than Adam Jones, the fact that he puts so many fewer balls into play hurts him.

I make two assumptions for this analysis. I assume that Adam Jones will hit the same proportion of pitches in the strike zone or not in the strike zone if he makes contact less frequently and that his production when he does make contact won’t increase if he swings less frequently. Preliminary research suggests that these assumptions are slightly inaccurate. He should be expected to make slightly better contact if he swings less frequently.

The wOBA All Balls Field projects how Adam Jones would perform if the extra in play contact was solely against pitches out of the strike zone and thus balances out both of the assumptions in the paragraph below. Even still, one would rather have Adam Jones’ numbers than Chris Davis’s strikeout and walk numbers. In reality, as shown in the chart above, Chris Davis’s plate discipline is about as good as Jonathan Schoop’s and only slightly better than Jimmy Paredes.

This poor plate discipline shows why it’s so hard for Chris Davis to be successful.  My metric measuring plate discipline suggests that he’s typically in the bottom 2% in this regard (5th percentile in 2014). He’s able to thrive because in 2013 and 2015, he was in the 99th percentile of wOBA for pitches hit into play. He was elite in those years because he was able to kill the ball whenever he hit it. In 2014, he was in the 86th percentile in wOBA for pitches hit into play. As soon he drops from elite to very good in wOBA for pitches put into play, he becomes an average hitter.

As the chart below shows, when he’s in the 99th percentile for wOBA for pitches in play, he’s in the top five percent of all batters. If he drops to just the 95th percentile for wOBA for pitches in play, all of a sudden he’s down to the 77th percentile of all batters. If he drops to the 90th percentile, then he’s in the 65th percentile of all batters. Once he drops to the 75th percentile for wOBA for pitches in play, he’s in the 36th percentile of all batters and is a DFA candidate. The takeaway is that he can still be one of the best batters when putting pitches into play and still be worthless. The chart below shows where he ranks based on his wOBA for pitches in play.

The news gets even worse. On Twitter, Jon stated that he felt that Chris Davis had a good chance of becoming ineffective due to a collapse in offense if his contact rate drops any further because his walking ability is an indirect effect of his hard contact. I figured that was unlikely because I presumed that Davis will be average offensively before his ability to make hard contact degrades significantly. But when I looked at the data, it became clear that one could argue that Davis always had elite numbers when putting pitches into play but struggled earlier in his career because he hits few pitches in the strike zone compared to other batters and had a poor walk vs. strikeout ratio.

In addition, while Adam Dunn, Carlos Pena, and Jarrod Saltalamacchia each became ineffective once their wOBA when putting pitches into play degraded, it would appear that Melvin Upton and Ryan Howard started to become ineffective when their plate discipline degraded. It seems reasonable to presume that Chris Davis will start struggling if his primary competency of hammering pitches put into play starts to falter or if his plate discipline becomes significantly worse. It’s reasonable to argue that Jon and I both have reasonable chances of being right.

Chris Davis probably won’t become ineffective immediately, but should before his contract ends. There are a few encouraging outliers though. David Ortiz didn’t stop being elite in this regard until he was 36 while Nelson Cruz is still elite at 35. Davis’s contract only runs through 36 so if he can take after those players then the Orioles will likely be happy with the results of this signing. On the other hand, Ryan Howard started struggling at 32 and Adam Dunn started struggling at 31. This contract may look ugly if the Orioles only receive three years of strong performance from Davis.

This article shows that most players would rather have Adam Jones’ walk and strikeout percentages rather than Chris Davis. If so, this suggests that plate discipline is largely misunderstood and that a metric similar to K-BB should be used for both hitters and pitchers. With a few exceptions for players like Nori Aoki and Alberto Callaspo, it is better to put the ball into play than not. Indeed, in 2013, Chris Davis had a .657 wOBA when putting a pitch in the strike zone into play while a player that walked in every plate appearance would have a .690 wOBA.  It's easiest for batters to be successful by putting the ball into play.

Breaking Down wOBA

Weighted On-Base Average (wOBA) is considered one of the best stats to quantify batting performance.  Per Fangraphs, wOBA combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value. But there are a number of questions about wOBA. Is it more important to have a strong BB/K rate or to produce when making contact? Is production when hitting fly balls more important than hitting ground balls and if so by how much? In order to answer these questions and more, I used 2013-2015 data from ESPN Stats and Information and Pitch FX to see what correlates best with overall wOBA.

The first test I ran used data from ESPN Stats and Information. I measured players wOBA based on contact against pitches thrown in the strike zone, wOBA on contact against pitches not in the strike zone and wOBA based on at bats that ended in either a hit by pitch, strikeout or unintentional walk. Then, I determined a given player’s percentage rank for each of these three categories from 2013-2015 as well as his rank for the likelihood of each of these events occurring.

I found that the average wOBA for pitches put into play in the strike zone was .385, for pitches out of the strike zone was .290 and for pitches not put into play was .204. I expected pitches hit in the strike zone to be the most productive type, but I was surprised to see that contact made against pitches thrown outside of the strike zone was more productive than at-bats not resulting in contact.

This indicates that batters are stuck in a game theory situation since they want to make contact as often as possible. Swinging aggressively may result in higher contact rates but also more strikeouts, fewer walks and potentially less productive contact. This means they need to decide whether to swing at a bad pitch early in the count. Ideally, batters would never walk or strikeout because they have their best results when making contact. On average, they should be willing to give up roughly 11 walks in order to prevent 9 strikeouts.  For comparison, a player like Adam Jones roughly strikes out 5 times for each time he earns an unintentional walk.

A stepwise regression analysis suggests that wOBA for pitches hit inside the strike zone percentile rank is the variable with the strongest relationship to actual wOBA percentile rank. The next most influential variable was wOBA on pitches which weren’t put into play. On average, 85% of all at bats end in one of the two above scenarios. There is a weaker relationship between actual wOBA rank and the percentage of balls in each of the three categories, suggesting that this has some relevance but not a huge amount. In general, the percent of pitches resulted in strikes or balls put into play had a positive impact on total wOBA (obviously, putting strikes into play is better than putting balls into play) while failing to put a ball into play had a negative impact.

The R^2 for this analysis was .9225 indicating that these wOBA categories accurately describe overall wOBA. This is expected but important to verify.

There is a high year-to-year correlation between how often a batter puts a pitch in the strike zone (.746) or out of the strike zone into play (.725) or alternatively fails to put the ball into play (.836). There is some year-to-year correlation between a batter’s ability to produce when putting pitches in the strike zone into play (.544) or when not putting the ball into play at all (.639), but only minimal when putting pitches not in the strike zone into play (.169). This suggests that players largely have the same outline from year-to-year but their production is considerably more variable. It also suggests that production on pitcher-friendly pitches is considerably more variable than production on hitter-friendly pitches. It also suggests that while these results have some predictive value, they’re more helpful for illustrating actual results.

The second test I ran used PITCHf/x data from 2013-2015 and studied players that faced at least 1000 pitches in a given season. I then used zone information to determine whether a pitch was a clear strike, a clear ball or unclear defined as being within 1 inch of the strike zone in any direction. Then I determined a players’ overall wOBA percentile rank as well as his wOBA rank for pitches that are strikes, balls and unclear as well as the likelihood percentile rank of him putting a ball, strike or unclear pitch into play.

Unsurprisingly, batters were most successful against pitches in the strike zone with a .411 wOBA, worse against pitches that were questionable with a .342 wOBA and worst against pitches that were clearly balls with a .284 wOBA. It’s pretty clear that batters are most successful when swinging at strikes.

Roughly two-thirds of balls put into play were strikes. As a result, it should not be surprising that a regression analysis indicated that how a player does against strikes has the largest impact on his wOBA by a significant margin. Performance against pitches that are unclear or not in the strike zone are significant variables but with minimal impact. The model’s R^2 was .96 suggesting that these components do accurately describe what occurred.

As with the ESPN data, a players’ year-to-year profile stays reasonably static. There’s a strong correlation of about .73 between a players current “In Play Percentile Ranks” and his rankings the following year for pitches in the strike zone or pitches not in the strike zone. There is a smaller correlation of .516 between a players’ current “wOBA against strikes” percentile rank and his rank in the following year. All in all, it shows that we can be reasonably certain that a player who swings at good or bad pitches will continue to do so in the future, but that this dataset is better used to describe what happened rather than to predict what will happen. This chart can be found below:

The third dataset that I looked at was also PitchF/x data from 2013-2015 based on players that faced at least 1000 pitches in a year. For this dataset, I determined their wOBA percentile rank based on batted ball type (fly ball, ground ball, line drive and pop up).

As one might suspect, batters were most productive hitting line drives with a .732 wOBA, flyballs ranked second with a .354 wOBA, grounders were third with a .251 wOBA and pop-ups were fourth with a .022 wOBA.

A regression analysis suggested that fly balls were more predictive of future wOBA than line drives. Hitting a larger percent of fly balls and line drives resulted in a higher wOBA while hitting grounders and pop ups resulted in a lower wOBA. An R^2 of .91 suggests that these categories accurately describe what occurred.

I found a moderate year-to-year correlation for the likelihood of a player being ranked at a given percentile for his profile type. I also found a reasonable year-to-year correlation for a player hitting fly balls, line drives and pop-ups but only a minimal one for a player hitting ground balls. A chart summarizing this data can be found here:

All in all, these datasets largely show things that are obvious. They show that hitters do better against pitches in the strike zone than against pitches that aren’t. They show that hitters are more productive when hitting line drives than when hitting ground balls. But they also show a few things that aren’t obvious. They illustrate how strikeouts, walks and hit by pitches relate to balls put into play. And they also can illustrate a player’s strengths and weaknesses.

Being able to visualize the data like this can lead to some surprising findings. Hopefully I’ll get a chance to show you some.